Pandas Profiling Report

Dataset statistics

Number of variables	22
Number of observations	1647
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	283.2 KiB
Average record size in memory	176.1 B

Variable types

Numeric	11
Categorical	11

Alerts

`agency` has constant value "DPR"	Constant
`agency_name` has constant value "Department_of_Parks_and_Recreation"	Constant
`state` has constant value "NY"	Constant
`Created_Date` has a high cardinality: 461 distinct values	High cardinality
`Closed_Date` has a high cardinality: 484 distinct values	High cardinality
`unique_key` is highly correlated with `Created_Year` and 1 other fields	High correlation
`incident_zip` is highly correlated with `longitude`	High correlation
`latitude` is highly correlated with `longitude`	High correlation
`longitude` is highly correlated with `incident_zip` and 1 other fields	High correlation
`Created_Year` is highly correlated with `unique_key` and 2 other fields	High correlation
`Created_Month_Number` is highly correlated with `Created_Year`	High correlation
`Closed_Year` is highly correlated with `unique_key` and 1 other fields	High correlation
`unique_key` is highly correlated with `Created_Year` and 1 other fields	High correlation
`incident_zip` is highly correlated with `longitude`	High correlation
`latitude` is highly correlated with `longitude`	High correlation
`longitude` is highly correlated with `incident_zip` and 1 other fields	High correlation
`Created_Year` is highly correlated with `unique_key` and 1 other fields	High correlation
`Closed_Year` is highly correlated with `unique_key` and 1 other fields	High correlation
`unique_key` is highly correlated with `Created_Year` and 1 other fields	High correlation
`Created_Year` is highly correlated with `unique_key` and 2 other fields	High correlation
`Created_Month_Number` is highly correlated with `Created_Year`	High correlation
`Closed_Year` is highly correlated with `unique_key` and 1 other fields	High correlation
`state` is highly correlated with `status` and 7 other fields	High correlation
`status` is highly correlated with `state` and 2 other fields	High correlation
`agency_name` is highly correlated with `state` and 7 other fields	High correlation
`Created_Month` is highly correlated with `state` and 3 other fields	High correlation
`Closed_Month` is highly correlated with `state` and 2 other fields	High correlation
`borough` is highly correlated with `state` and 3 other fields	High correlation
`city` is highly correlated with `state` and 3 other fields	High correlation
`complaint_type` is highly correlated with `state` and 3 other fields	High correlation
`agency` is highly correlated with `state` and 7 other fields	High correlation
`CID` is highly correlated with `unique_key` and 7 other fields	High correlation
`unique_key` is highly correlated with `CID` and 7 other fields	High correlation
`complaint_type` is highly correlated with `CID` and 5 other fields	High correlation
`city` is highly correlated with `borough` and 3 other fields	High correlation
`borough` is highly correlated with `city` and 3 other fields	High correlation
`incident_zip` is highly correlated with `city` and 3 other fields	High correlation
`latitude` is highly correlated with `city` and 3 other fields	High correlation
`longitude` is highly correlated with `city` and 3 other fields	High correlation
`Created_Year` is highly correlated with `CID` and 5 other fields	High correlation
`Created_Month` is highly correlated with `CID` and 7 other fields	High correlation
`Created_Month_Number` is highly correlated with `CID` and 7 other fields	High correlation
`Created_Day` is highly correlated with `CID` and 1 other fields	High correlation
`Closed_Year` is highly correlated with `unique_key` and 4 other fields	High correlation
`Closed_Month` is highly correlated with `CID` and 7 other fields	High correlation
`Closed_Month_Number` is highly correlated with `CID` and 6 other fields	High correlation
`CID` is uniformly distributed	Uniform
`CID` has unique values	Unique
`unique_key` has unique values	Unique

Reproduction

Analysis started	2021-12-03 01:41:10.147699
Analysis finished	2021-12-03 01:41:28.724369
Duration	18.58 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

CID
Real number (ℝ_≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	1647
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	823

Minimum	0
Maximum	1646
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	0
5-th percentile	82.3
Q1	411.5
median	823
Q3	1234.5
95-th percentile	1563.7
Maximum	1646
Range	1646
Interquartile range (IQR)	823

Descriptive statistics

Standard deviation	475.5922623
Coefficient of variation (CV)	0.5778763819
Kurtosis	-1.2
Mean	823
Median Absolute Deviation (MAD)	412
Skewness	0
Sum	1355481
Variance	226188
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1	0.1%
541	1	0.1%
561	1	0.1%
559	1	0.1%
557	1	0.1%
555	1	0.1%
553	1	0.1%
551	1	0.1%
549	1	0.1%
547	1	0.1%
Other values (1637)	1637	99.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	0.1%
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
5	1	0.1%
6	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%

Value	Count	Frequency (%)
1646	1	0.1%
1645	1	0.1%
1644	1	0.1%
1643	1	0.1%
1642	1	0.1%
1641	1	0.1%
1640	1	0.1%
1639	1	0.1%
1638	1	0.1%
1637	1	0.1%

unique_key
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	1647
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	39508745.86

Minimum	17573251
Maximum	52237113
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	17573251
5-th percentile	17593857.7
Q1	40328785
median	43190556
Q3	45725472
95-th percentile	46240173.8
Maximum	52237113
Range	34663862
Interquartile range (IQR)	5396687

Descriptive statistics

Standard deviation	10065780.58
Coefficient of variation (CV)	0.2547734776
Kurtosis	0.6697275797
Mean	39508745.86
Median Absolute Deviation (MAD)	2571312
Skewness	-1.553084839
Sum	6.507090443 × 10¹⁰
Variance	1.013199387 × 10¹⁴
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
42766338	1	0.1%
39836266	1	0.1%
45290491	1	0.1%
42650242	1	0.1%
42800154	1	0.1%
43478109	1	0.1%
36553341	1	0.1%
45374074	1	0.1%
40471160	1	0.1%
39840374	1	0.1%
Other values (1637)	1637	99.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
17573251	1	0.1%
17574216	1	0.1%
17574396	1	0.1%
17574540	1	0.1%
17576203	1	0.1%
17576302	1	0.1%
17576344	1	0.1%
17576350	1	0.1%
17576414	1	0.1%
17576688	1	0.1%

Value	Count	Frequency (%)
52237113	1	0.1%
52191184	1	0.1%
52137035	1	0.1%
51993389	1	0.1%
51717754	1	0.1%
51661797	1	0.1%
51588872	1	0.1%
51131439	1	0.1%
51091152	1	0.1%
50981348	1	0.1%

agency
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

DPR	1647

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	DPR
2nd row	DPR
3rd row	DPR
4th row	DPR
5th row	DPR

Common Values

Value	Count	Frequency (%)
DPR	1647	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
dpr	1647	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

agency_name
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

Department_of_Parks_and_Recreation	1647

Length

Max length	34
Median length	34
Mean length	34
Min length	34

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Department_of_Parks_and_Recreation
2nd row	Department_of_Parks_and_Recreation
3rd row	Department_of_Parks_and_Recreation
4th row	Department_of_Parks_and_Recreation
5th row	Department_of_Parks_and_Recreation

Common Values

Value	Count	Frequency (%)
Department_of_Parks_and_Recreation	1647	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
department_of_parks_and_recreation	1647	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

complaint_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

Dead/Dying_Tree	598
Illegal_Tree_Damage	572
Damaged_Tree	477

Length

Max length	19
Median length	15
Mean length	15.52034001
Min length	12

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Damaged_Tree
2nd row	Damaged_Tree
3rd row	Damaged_Tree
4th row	Damaged_Tree
5th row	Damaged_Tree

Common Values

Value	Count	Frequency (%)
Dead/Dying_Tree	598	36.3%
Illegal_Tree_Damage	572	34.7%
Damaged_Tree	477	29.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
dead/dying_tree	598	36.3%
illegal_tree_damage	572	34.7%
damaged_tree	477	29.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

status
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

Closed	1641
In Progress	6

Length

Max length	11
Median length	6
Mean length	6.018214936
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Closed
2nd row	Closed
3rd row	Closed
4th row	Closed
5th row	Closed

Common Values

Value	Count	Frequency (%)
Closed	1641	99.6%
In Progress	6	0.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
closed	1641	99.3%
in	6	0.4%
progress	6	0.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

city
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	44
Distinct (%)	2.7%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

Brooklyn	535
Staten_Island	180
New_York	165
Bronx	114
Flushing	66
Other values (39)	587

Length

Max length	19
Median length	8
Mean length	9.305403764
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Ridgewood
2nd row	Brooklyn
3rd row	Bayside
4th row	Brooklyn
5th row	Bellerose

Common Values

Value	Count	Frequency (%)
Brooklyn	535	32.5%
Staten_Island	180	10.9%
New_York	165	10.0%
Bronx	114	6.9%
Flushing	66	4.0%
Jamaica	54	3.3%
Astoria	44	2.7%
Whitestone	43	2.6%
Ridgewood	29	1.8%
Fresh_Meadows	28	1.7%
Other values (34)	389	23.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
brooklyn	535	32.5%
staten_island	180	10.9%
new_york	165	10.0%
bronx	114	6.9%
flushing	66	4.0%
jamaica	54	3.3%
astoria	44	2.7%
whitestone	43	2.6%
ridgewood	29	1.8%
fresh_meadows	28	1.7%
Other values (34)	389	23.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

borough
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

Queens	653
Brooklyn	535
Staten_Island	180
Manhattan	165
Bronx	114

Length

Max length	13
Median length	8
Mean length	7.646023072
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Queens
2nd row	Brooklyn
3rd row	Queens
4th row	Brooklyn
5th row	Queens

Common Values

Value	Count	Frequency (%)
Queens	653	39.6%
Brooklyn	535	32.5%
Staten_Island	180	10.9%
Manhattan	165	10.0%
Bronx	114	6.9%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
queens	653	39.6%
brooklyn	535	32.5%
staten_island	180	10.9%
manhattan	165	10.0%
bronx	114	6.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

incident_zip
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	162
Distinct (%)	9.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	11002.62356

Minimum	10001
Maximum	11694
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	10001
5-th percentile	10022
Q1	10466
median	11221
Q3	11364
95-th percentile	11427
Maximum	11694
Range	1693
Interquartile range (IQR)	898

Descriptive statistics

Standard deviation	488.7673827
Coefficient of variation (CV)	0.04442280336
Kurtosis	-0.6669087162
Mean	11002.62356
Median Absolute Deviation (MAD)	151
Skewness	-0.9873939765
Sum	18121321
Variance	238893.5544
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
11357	43	2.6%
11234	41	2.5%
10312	37	2.2%
10314	35	2.1%
11230	35	2.1%
11215	31	1.9%
11209	29	1.8%
11385	29	1.8%
10306	23	1.4%
11358	22	1.3%
Other values (152)	1322	80.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
10001	5	0.3%
10002	11	0.7%
10003	5	0.3%
10009	6	0.4%
10011	11	0.7%
10012	5	0.3%
10013	3	0.2%
10014	8	0.5%
10016	5	0.3%
10017	1	0.1%

Value	Count	Frequency (%)
11694	7	0.4%
11691	5	0.3%
11436	8	0.5%
11435	6	0.4%
11434	14	0.9%
11433	10	0.6%
11432	16	1.0%
11429	10	0.6%
11428	5	0.3%
11427	9	0.5%

latitude
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	1571
Distinct (%)	95.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	40.69924573

Minimum	40.50190414
Maximum	40.91107395
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	40.50190414
5-th percentile	40.56550636
Q1	40.6336399
median	40.69852462
Q3	40.75537135
95-th percentile	40.85004618
Maximum	40.91107395
Range	0.40916981
Interquartile range (IQR)	0.121731445

Descriptive statistics

Standard deviation	0.08152149339
Coefficient of variation (CV)	0.002003022216
Kurtosis	-0.2659598809
Mean	40.69924573
Median Absolute Deviation (MAD)	0.06070443
Skewness	0.1464206562
Sum	67031.65772
Variance	0.006645753885
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
40.88562974	6	0.4%
40.75500475	3	0.2%
40.628261	3	0.2%
40.77331721	3	0.2%
40.68524221	3	0.2%
40.76790461	3	0.2%
40.71740699	3	0.2%
40.5292104	3	0.2%
40.8551972	2	0.1%
40.71614795	2	0.1%
Other values (1561)	1616	98.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
40.50190414	1	0.1%
40.50526752	2	0.1%
40.50709698	1	0.1%
40.50741188	1	0.1%
40.50927191	1	0.1%
40.51393834	1	0.1%
40.51459924	1	0.1%
40.52723277	1	0.1%
40.52785073	1	0.1%
40.52816389	1	0.1%

Value	Count	Frequency (%)
40.91107395	1	0.1%
40.9086316	1	0.1%
40.9074933	1	0.1%
40.90525418	1	0.1%
40.90190684	1	0.1%
40.89870485	1	0.1%
40.89783947	1	0.1%
40.8975924	1	0.1%
40.8971504	1	0.1%
40.89513239	1	0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	1571
Distinct (%)	95.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-73.92060394

Minimum	-74.25319469
Maximum	-73.70143666
Zeros	0
Zeros (%)	0.0%
Negative	1647
Negative (%)	100.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	-74.25319469
5-th percentile	-74.15105504
Q1	-73.98335184
median	-73.9249353
Q3	-73.82995363
95-th percentile	-73.74141335
Maximum	-73.70143666
Range	0.55175803
Interquartile range (IQR)	0.15339821

Descriptive statistics

Standard deviation	0.1143565406
Coefficient of variation (CV)	-0.001547018483
Kurtosis	0.01330237305
Mean	-73.92060394
Median Absolute Deviation (MAD)	0.06919525
Skewness	-0.4191522725
Sum	-121747.2347
Variance	0.01307741837
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
-73.86605482	6	0.4%
-73.82414688	3	0.2%
-74.19088392	3	0.2%
-74.02765337	3	0.2%
-73.96685821	3	0.2%
-73.74887454	3	0.2%
-73.95714428	3	0.2%
-73.98463631	3	0.2%
-74.25319469	2	0.1%
-74.10453133	2	0.1%
Other values (1561)	1616	98.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-74.25319469	2	0.1%
-74.25083997	1	0.1%
-74.24913564	1	0.1%
-74.2473424	1	0.1%
-74.24624324	1	0.1%
-74.24593999	1	0.1%
-74.243082	1	0.1%
-74.22119788	1	0.1%
-74.22106038	1	0.1%
-74.22034832	1	0.1%

Value	Count	Frequency (%)
-73.70143666	1	0.1%
-73.70233953	1	0.1%
-73.70237702	1	0.1%
-73.70268696	1	0.1%
-73.70293887	1	0.1%
-73.70296026	1	0.1%
-73.70353144	1	0.1%
-73.70387775	1	0.1%
-73.70401747	1	0.1%
-73.70409587	1	0.1%

Created_Year
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	6
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2017.686096

Minimum	2010
Maximum	2021
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	2010
5-th percentile	2010
Q1	2018
median	2019
Q3	2020
95-th percentile	2020
Maximum	2021
Range	11
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	3.590414503
Coefficient of variation (CV)	0.001779471301
Kurtosis	0.7296685896
Mean	2017.686096
Median Absolute Deviation (MAD)	1
Skewness	-1.58866897
Sum	3323129
Variance	12.8910763
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
2019	624	37.9%
2020	576	35.0%
2010	286	17.4%
2018	108	6.6%
2017	41	2.5%
2021	12	0.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2010	286	17.4%
2017	41	2.5%
2018	108	6.6%
2019	624	37.9%
2020	576	35.0%
2021	12	0.7%

Value	Count	Frequency (%)
2021	12	0.7%
2020	576	35.0%
2019	624	37.9%
2018	108	6.6%
2017	41	2.5%
2010	286	17.4%

Created_Month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

May	432
July	360
June	160
April	117
January	100
Other values (7)	478

Length

Max length	9
Median length	4
Mean length	4.889496053
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	December
2nd row	July
3rd row	July
4th row	July
5th row	December

Common Values

Value	Count	Frequency (%)
May	432	26.2%
July	360	21.9%
June	160	9.7%
April	117	7.1%
January	100	6.1%
December	96	5.8%
March	94	5.7%
February	89	5.4%
August	79	4.8%
September	59	3.6%
Other values (2)	61	3.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
may	432	26.2%
july	360	21.9%
june	160	9.7%
april	117	7.1%
january	100	6.1%
december	96	5.8%
march	94	5.7%
february	89	5.4%
august	79	4.8%
september	59	3.6%
Other values (2)	61	3.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Created_Month_Number
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.830601093

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	5
median	5
Q3	7
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.626130226
Coefficient of variation (CV)	0.4504047155
Kurtosis	0.2145281585
Mean	5.830601093
Median Absolute Deviation (MAD)	2
Skewness	0.3865640598
Sum	9603
Variance	6.896559967
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
5	432	26.2%
7	360	21.9%
6	160	9.7%
4	117	7.1%
1	100	6.1%
12	96	5.8%
3	94	5.7%
2	89	5.4%
8	79	4.8%
9	59	3.6%
Other values (2)	61	3.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	100	6.1%
2	89	5.4%
3	94	5.7%
4	117	7.1%
5	432	26.2%
6	160	9.7%
7	360	21.9%
8	79	4.8%
9	59	3.6%
10	51	3.1%

Value	Count	Frequency (%)
12	96	5.8%
11	10	0.6%
10	51	3.1%
9	59	3.6%
8	79	4.8%
7	360	21.9%
6	160	9.7%
5	432	26.2%
4	117	7.1%
3	94	5.7%

Created_Day
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	31
Distinct (%)	1.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.81542198

Minimum	1
Maximum	31
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	9
median	15
Q3	23
95-th percentile	30
Maximum	31
Range	30
Interquartile range (IQR)	14

Descriptive statistics

Standard deviation	8.359695017
Coefficient of variation (CV)	0.5285786891
Kurtosis	-1.064411667
Mean	15.81542198
Median Absolute Deviation (MAD)	7
Skewness	0.0779890187
Sum	26048
Variance	69.88450078
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
13	132	8.0%
6	76	4.6%
20	74	4.5%
11	66	4.0%
12	65	3.9%
22	58	3.5%
30	57	3.5%
9	57	3.5%
26	57	3.5%
21	56	3.4%
Other values (21)	949	57.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	43	2.6%
2	34	2.1%
3	40	2.4%
4	42	2.6%
5	39	2.4%
6	76	4.6%
7	50	3.0%
8	55	3.3%
9	57	3.5%
10	49	3.0%

Value	Count	Frequency (%)
31	27	1.6%
30	57	3.5%
29	48	2.9%
28	50	3.0%
27	40	2.4%
26	57	3.5%
25	49	3.0%
24	49	3.0%
23	37	2.2%
22	58	3.5%

Created_Date
Categorical

HIGH CARDINALITY

Distinct	461
Distinct (%)	28.0%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

2010-07-13	82
2010-07-06	54
2010-07-09	23
2020-05-20	22
2019-12-30	21
Other values (456)	1445

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	195 ?
Unique (%)	11.8%

Sample

1st row	2019-12-25
2nd row	2010-07-03
3rd row	2019-07-05
4th row	2010-07-03
5th row	2019-12-26

Common Values

Value	Count	Frequency (%)
2010-07-13	82	5.0%
2010-07-06	54	3.3%
2010-07-09	23	1.4%
2020-05-20	22	1.3%
2019-12-30	21	1.3%
2020-05-12	20	1.2%
2020-05-15	18	1.1%
2020-05-19	18	1.1%
2010-07-04	17	1.0%
2020-05-11	17	1.0%
Other values (451)	1355	82.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2010-07-13	82	5.0%
2010-07-06	54	3.3%
2010-07-09	23	1.4%
2020-05-20	22	1.3%
2019-12-30	21	1.3%
2020-05-12	20	1.2%
2020-05-15	18	1.1%
2020-05-19	18	1.1%
2010-07-04	17	1.0%
2020-05-11	17	1.0%
Other values (451)	1355	82.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Closed_Year
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	10
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2017.986035

Minimum	2010
Maximum	2021
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	2010
5-th percentile	2010
Q1	2019
median	2020
Q3	2020
95-th percentile	2020
Maximum	2021
Range	11
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	3.618895229
Coefficient of variation (CV)	0.001793320254
Kurtosis	0.867126401
Mean	2017.986035
Median Absolute Deviation (MAD)	1
Skewness	-1.632307395
Sum	3323623
Variance	13.09640268
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
2020	759	46.1%
2019	494	30.0%
2010	246	14.9%
2021	71	4.3%
2017	29	1.8%
2011	23	1.4%
2012	9	0.5%
2018	8	0.5%
2013	4	0.2%
2014	4	0.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2010	246	14.9%
2011	23	1.4%
2012	9	0.5%
2013	4	0.2%
2014	4	0.2%
2017	29	1.8%
2018	8	0.5%
2019	494	30.0%
2020	759	46.1%
2021	71	4.3%

Value	Count	Frequency (%)
2021	71	4.3%
2020	759	46.1%
2019	494	30.0%
2018	8	0.5%
2017	29	1.8%
2014	4	0.2%
2013	4	0.2%
2012	9	0.5%
2011	23	1.4%
2010	246	14.9%

Closed_Month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

July	305
June	183
May	165
March	151
December	131
Other values (7)	712

Length

Max length	9
Median length	5
Mean length	5.618093503
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	January
2nd row	July
3rd row	July
4th row	July
5th row	March

Common Values

Value	Count	Frequency (%)
July	305	18.5%
June	183	11.1%
May	165	10.0%
March	151	9.2%
December	131	8.0%
April	122	7.4%
January	115	7.0%
August	112	6.8%
September	107	6.5%
October	95	5.8%
Other values (2)	161	9.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
july	305	18.5%
june	183	11.1%
may	165	10.0%
march	151	9.2%
december	131	8.0%
april	122	7.4%
january	115	7.0%
august	112	6.8%
september	107	6.5%
october	95	5.8%
Other values (2)	161	9.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Closed_Month_Number
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	12
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.321190043

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	4
median	6
Q3	8
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	3.102060075
Coefficient of variation (CV)	0.4907398851
Kurtosis	-0.770053615
Mean	6.321190043
Median Absolute Deviation (MAD)	2
Skewness	0.1166877643
Sum	10411
Variance	9.622776712
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
7	305	18.5%
6	183	11.1%
5	165	10.0%
3	151	9.2%
12	131	8.0%
4	122	7.4%
1	115	7.0%
8	112	6.8%
9	107	6.5%
2	95	5.8%
Other values (2)	161	9.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	115	7.0%
2	95	5.8%
3	151	9.2%
4	122	7.4%
5	165	10.0%
6	183	11.1%
7	305	18.5%
8	112	6.8%
9	107	6.5%
10	95	5.8%

Value	Count	Frequency (%)
12	131	8.0%
11	66	4.0%
10	95	5.8%
9	107	6.5%
8	112	6.8%
7	305	18.5%
6	183	11.1%
5	165	10.0%
4	122	7.4%
3	151	9.2%

Closed_Day
Real number (ℝ_≥0)

Distinct	31
Distinct (%)	1.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.80327869

Minimum	1
Maximum	31
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.0 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	9
median	16
Q3	23
95-th percentile	29
Maximum	31
Range	30
Interquartile range (IQR)	14

Descriptive statistics

Standard deviation	8.430412107
Coefficient of variation (CV)	0.5334596873
Kurtosis	-1.089960228
Mean	15.80327869
Median Absolute Deviation (MAD)	7
Skewness	-0.03965529037
Sum	26028
Variance	71.0718483
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
26	91	5.5%
23	87	5.3%
12	78	4.7%
13	76	4.6%
20	71	4.3%
19	66	4.0%
14	62	3.8%
16	60	3.6%
17	59	3.6%
6	58	3.5%
Other values (21)	939	57.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	49	3.0%
2	51	3.1%
3	57	3.5%
4	45	2.7%
5	35	2.1%
6	58	3.5%
7	39	2.4%
8	57	3.5%
9	48	2.9%
10	54	3.3%

Value	Count	Frequency (%)
31	28	1.7%
30	42	2.6%
29	32	1.9%
28	38	2.3%
27	50	3.0%
26	91	5.5%
25	32	1.9%
24	45	2.7%
23	87	5.3%
22	53	3.2%

Closed_Date
Categorical

HIGH CARDINALITY

Distinct	484
Distinct (%)	29.4%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

2020-12-23	40
2020-03-26	27
2010-07-13	21
2010-07-14	19
2019-06-12	18
Other values (479)	1522

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	186 ?
Unique (%)	11.3%

Sample

1st row	2020-01-03
2nd row	2010-07-06
3rd row	2019-07-09
4th row	2010-07-22
5th row	2020-03-31

Common Values

Value	Count	Frequency (%)
2020-12-23	40	2.4%
2020-03-26	27	1.6%
2010-07-13	21	1.3%
2010-07-14	19	1.2%
2019-06-12	18	1.1%
2020-05-21	17	1.0%
2020-05-26	15	0.9%
2010-07-06	15	0.9%
2020-04-03	14	0.9%
2019-06-13	13	0.8%
Other values (474)	1448	87.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2020-12-23	40	2.4%
2020-03-26	27	1.6%
2010-07-13	21	1.3%
2010-07-14	19	1.2%
2019-06-12	18	1.1%
2020-05-21	17	1.0%
2020-05-26	15	0.9%
2010-07-06	15	0.9%
2020-04-03	14	0.9%
2020-02-26	13	0.8%
Other values (474)	1448	87.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

state
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	13.0 KiB

NY	1647

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	NY
2nd row	NY
3rd row	NY
4th row	NY
5th row	NY

Common Values

Value	Count	Frequency (%)
NY	1647	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
ny	1647	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	CID	unique_key	agency	agency_name	complaint_type	status	city	borough	incident_zip	latitude	longitude	Created_Year	Created_Month	Created_Month_Number	Created_Day	Created_Date	Closed_Year	Closed_Month	Closed_Month_Number	Closed_Day	Closed_Date	state
0	0	45244842	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Ridgewood	Queens	11385	40.704753	-73.884978	2019	December	12	25	2019-12-25	2020	January	1	3	2020-01-03	NY
1	1	17573251	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Brooklyn	Brooklyn	11208	40.672582	-73.877543	2010	July	7	3	2010-07-03	2010	July	7	6	2010-07-06	NY
2	2	43202583	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Bayside	Queens	11360	40.774356	-73.780371	2019	July	7	5	2019-07-05	2019	July	7	9	2019-07-09	NY
3	3	17574216	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Brooklyn	Brooklyn	11223	40.606472	-73.983261	2010	July	7	3	2010-07-03	2010	July	7	22	2010-07-22	NY
4	4	45245555	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Bellerose	Queens	11426	40.733696	-73.722079	2019	December	12	26	2019-12-26	2020	March	3	31	2020-03-31	NY
5	5	45245562	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Bronx	Bronx	10466	40.891522	-73.853887	2019	December	12	26	2019-12-26	2020	September	9	25	2020-09-25	NY
6	6	39498033	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Brooklyn	Brooklyn	11208	40.685603	-73.877425	2018	June	6	19	2018-06-19	2019	October	10	20	2019-10-20	NY
7	7	17574396	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Bayside	Queens	11360	40.783658	-73.777083	2010	July	7	3	2010-07-03	2010	September	9	10	2010-09-10	NY
8	8	17574540	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	Flushing	Queens	11358	40.769916	-73.791101	2010	July	7	3	2010-07-03	2010	July	7	6	2010-07-06	NY
9	9	44067986	DPR	Department_of_Parks_and_Recreation	Damaged_Tree	Closed	New_York	Manhattan	10025	40.801620	-73.962540	2019	October	10	16	2019-10-16	2019	October	10	17	2019-10-17	NY

Last rows

	CID	unique_key	agency	agency_name	complaint_type	status	city	borough	incident_zip	latitude	longitude	Created_Year	Created_Month	Created_Month_Number	Created_Day	Created_Date	Closed_Year	Closed_Month	Closed_Month_Number	Closed_Day	Closed_Date	state
1637	1637	42729347	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Brooklyn	Brooklyn	11216	40.673885	-73.955308	2019	May	5	20	2019-05-20	2020	March	3	13	2020-03-13	NY
1638	1638	43494341	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Brooklyn	Brooklyn	11238	40.674828	-73.966801	2019	August	8	8	2019-08-08	2019	August	8	12	2019-08-12	NY
1639	1639	43494342	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Brooklyn	Brooklyn	11228	40.623988	-74.008901	2019	August	8	8	2019-08-08	2019	September	9	3	2019-09-03	NY
1640	1640	42730769	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Jackson_Heights	Queens	11372	40.750911	-73.871447	2019	May	5	21	2019-05-21	2020	April	4	7	2020-04-07	NY
1641	1641	42732045	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Far_Rockaway	Queens	11691	40.593654	-73.753204	2019	May	5	21	2019-05-21	2019	October	10	3	2019-10-03	NY
1642	1642	42732088	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	New_York	Manhattan	10036	40.761185	-73.993217	2019	May	5	21	2019-05-21	2019	June	6	19	2019-06-19	NY
1643	1643	42734647	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Oakland_Gardens	Queens	11364	40.747261	-73.769463	2019	May	5	21	2019-05-21	2019	August	8	12	2019-08-12	NY
1644	1644	45875454	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Staten_Island	Staten_Island	10314	40.600315	-74.164407	2020	March	3	22	2020-03-22	2020	December	12	23	2020-12-23	NY
1645	1645	45875899	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	Staten_Island	Staten_Island	10301	40.645497	-74.085713	2020	March	3	22	2020-03-22	2020	March	3	24	2020-03-24	NY
1646	1646	42813797	DPR	Department_of_Parks_and_Recreation	Illegal_Tree_Damage	Closed	New_York	Manhattan	10027	40.812363	-73.961443	2019	May	5	30	2019-05-30	2019	July	7	8	2019-07-08	NY

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values